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Hygiene condition of water use and sanitation facilities at urban slums: analysis of port city Chittagong, Bangladesh

Muhit I. B., Chowdhury S. T., Nahid N., Zaman M. F.

Civil and Environmental Engineering Reports

2015

No. 16

153--166

Low income community development is the prerequisite for the overall development of a society. There are different kinds of parameters to widen community development, such as health, economic, social, a living pattern, etc. Sanitation condition is the crucial aspect that is directly or indirectly inter bond with all the parameters. To see the exact reasons behind brutal unhygienic sanitation conditions of water supply and latrine system in a low cost community, the Chittagong City Corporation area has been picked. Relevant data have been collected from field survey, consultancy with inhabitants, Chittagong City Corporation, Power Development Board, and WASA. To know the possible reasons behind the water supply and germ-infested sanitation, state of a low cost community, this paper attempts to shed some light on the tribulations behind the scarcity of safe drinking water, dirt free a as well as sustainable latrine and drainage system and offensive water management.

Obszary takie jak rozwój gospodarczy, bariery społeczne, standard życia i zdrowie, ściśle związane są ze stanem wyposażenia sanitarnego. W artykule dokonano analizy warunków sanitarnych związanych z zaopatrzeniem w wodę i odprowadzeniem ścieków dla społeczności o niskich dochodach w obszarze Chittagong City Corporation. Dane zostały zebrane podczas ankietowych badań terenowych, rozmów z mieszkańcami, z bazy Chittagong City Corporation, Power Development Board i WASA.

A rainfall forecasting method using machine learning models and its application to the Fukuoka city case

Sumi S. M., Zaman M. F., Hirose H.

International Journal of Applied Mathematics and Computer Science

2012

Vol. 22, no. 4

841-854

In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.